Brrr: Cold, short and sad winter days

Coming from Peru to Scotland was not only a cultural shock but an environmental one, if you will. I had never experienced such strikingly beautiful but equally emotionally debilitating winters. Here is when I first heard the term ‘Seasonal Depression’ being thrown around at this time of the year, between the end of Autumn and the cusp of Winter. Whereas it’s a passing comment from a friend, a sitcom joke, or a genuine Google search during those short, dark, and cold winter days, ‘Seasonal Depression’, or medically classified as Seasonal Affective Disorder (SAD), has affected almost everyone I know here.
Therefore, this investigation seeks to quantify the verbal leads I have stumbled upon for years during conversations with friends and acquaintances.

Seasonal Affective Disorder (SAD)

SAD is widely discussed in literature, with a focus in populations living at high latitudes such as Denmark, Sweden and Iceland (REFERENCE et al., DATE). Mainland Scotland regionally situates itself on par with the southernmost areas of Scandinavian countries, with Edinburgh and Copenhagen being latitudinal buddies. However, the Scottish Isles (i.e, Orkney, Shetland, etc) are further north and are roughly comparable in latitude to Norwegian cities such as Bergen and Oslo. Hence, making the nature of this investigation as interesting as it close to my personal interests.

Investigation breakdown

This report focuses on investigating whether antidepressant medication prescribing in Scotland shows seasonal patterns and whether those patterns are related to regional daylight exposure and deprivation levels.

The research question to be explored asks:

To what extent do seasonal variations in daylight hours affect antidepressant medication prescribing across Scottish NHS Health Boards?

For this investigation, prescription data is taken as a direct inferential measure of the population-level mental health burden.

Hypotheses

This report puts forward an overarching hypothesis:

Null (H0): Antidepressant prescription rates do not have seasonal patterns.

Alternative (Ha): Antidepressant prescription rates have seasonal patterns.

Where within Ha, the following is proposed:

1a. Prescription rates increase during shorter months with shorter daylight hours and colder temperatures (autumn and winter) and decrease during those with longer daylight hours and warmer temperature (spring and summer).

In the assumption that Ha is true, this report proposed two further sub-hypotheses that further enrich the multi-factorial lens through which seasonal effects inlfuence prescription rates:

I. The latitude of prescribing NHS Health Boards further influences the seasonality of antidepressant prescription rates, the more northern a health board is, the heavier the impact of these seasonal patterns.

II. Areas with a higher socioeconomic deprivation index (SMID) will have a consistently higher benchmark antidepressant use regardless of season, making the impact of varying daylight hours and temperature be even greater.

Thus, the report tests whether: a) antidepressant prescriptions are higher in autumn/winter than in spring/summer, b) geographic latitude/region affects seasonal patterns, and c) more deprived areas show higher baseline antidepressant prescribing.

Variables

Variables were regionally and seasonally categorised according to Met Office UK guidelines. For information on specific categorisation see Appendix 1.1 and 1.2.

Antidepressant Prescriptions

Raw data from multiple institutional websites include all medications prescribed by NHS Health Boards. To differentiate and include only Antidepressant prescriptions, only prescriptions with a BNF item code starting with ‘0403’ were used.

This report is a multi-factorial exploration of the environmental and social dimensions of antidepressant prescription rates in Scotland given that it’s analysis focuses on three distinct aspects as shown below.

Datasets Used

The investigation window used is from March 2024 to February 2025 (inclusive of March 1st, 2024 - February 28th, 2025) to assess the potential cyclical nature of results and make adequate comparisons between seasons.
All links mentioned below are included in the code within Methodology.

Datasets

  • Public Health Scotland - Prescription Data: “Prescriptions in the Community (by Health Board)” for Jan 2024 - Jun 2025 (subset to fit investigation window).
  • Public Health Scotland - Population Data: “Health Board Population” from October 2024. Used for data standardisation (population-weighting).
  • Met Office Data: “Monthly, seasonal and annual total duration of bright sunshine for Scotland” per region (North, East, West). Converted from TXT to CSV files and stored in docs/data. See original TXT files in Appendix 1.3.
  • Public Health Scotland - Deprivation: “Scottish Index of Multiple Deprivation (SMID 2020)” for all GP practices and Health Boards.

Methodology

1. Data Loading and Wrangling

1.1 Loading all required libraries on RStudio

library(tidyverse)
library(here) # for the upkeep of the directory structure
library(janitor) # for data cleaning
library(lubridate)
library(gt) # for table building
library(sf) # for geospatial visualisation
library(ggplot2)
library(ggtext)
library(patchwork)
library(plotly)
library(cowplot)
library(stringr)

1.2 Loading all Health Board Data (January 2024:June 2025) at once

Utilising janitor package by using the clean_names() function to have uniform names throughout datasets once the all prescription datasets are loaded.

urls_prescr <- list(
  prescr_jan_june_2024 <- "https://www.opendata.nhs.scot/dataset/84393984-14e9-4b0d-a797-b288db64d088/resource/f0df380b-3f9b-4536-bb87-569e189b727a/download/hb_pitc2024_01_06-1.csv",
  prescr_july_dec_2024 <- "https://www.opendata.nhs.scot/dataset/84393984-14e9-4b0d-a797-b288db64d088/resource/f3b9f2e2-66c0-4310-9b8e-734781d2ed0a/download/hb_pitc2024_07_12-1.csv",
  prescr_jan_june_2025 <- "https://www.opendata.nhs.scot/dataset/84393984-14e9-4b0d-a797-b288db64d088/resource/9de908b3-9c28-4cc3-aa32-72350a0579d1/download/hb_pitc2025_01_06.csv")

# Reads all Health Board prescription data in a loop to avoid repetition
prescr_list <- map(urls_prescr,
                   ~read_csv(.x) %>%
                     clean_names())

# Ainds together everything in a single tibble
prescr_raw <- bind_rows(prescr_list, .id = "source_file") %>% 
  mutate(paid_date_month = str_trim(as.character(paid_date_month))) %>% 
  select(-source_file)

glimpse(prescr_raw)
## Rows: 2,249,380
## Columns: 9
## $ hbt                   <chr> "S08000015", "S08000015", "S08000015", "S0800001…
## $ dmd_code              <dbl> 1.001011e+15, 1.001411e+15, 1.001811e+15, 1.0018…
## $ bnf_item_code         <chr> "0603020J0AAAEAE", "1001010P0AAAHAH", "1310012F0…
## $ bnf_item_description  <chr> "HYDROCORTISONE 20MG TABLETS", "NAPROXEN 250MG G…
## $ prescribed_type       <chr> "VMP", "VMP", "VMP", "VMPP", "VMP", "VMPP", "VMP…
## $ number_of_paid_items  <dbl> 25, 53, 275, 1, 181, 2, 487, 1432, 66, 1, 1, 283…
## $ paid_quantity         <dbl> 1244, 4046, 4695, 15, 25320, 240, 24924, 65820, …
## $ gross_ingredient_cost <dbl> 145.58, 187.17, 1111.15, 3.55, 4093.40, 38.80, 5…
## $ paid_date_month       <chr> "202401", "202401", "202401", "202401", "202401"…

1.3 Selecting only antidepressant medication prescriptions

Filtering out by “bnf_item_code” to keep only those prescriptions that are antidepressants (codes starting with ‘0403’) and that were prescribed within investigation window.

# Only keeping antidepressant codes and aggregating prescriptions per HB per month
prescr_monthly <- prescr_raw %>%
  filter(!is.na(bnf_item_code)) %>% 
  filter(str_detect(bnf_item_code, "^0403")) %>%
  mutate(paid_date_month = as.integer(paid_date_month)) %>% 
  group_by(hbt, paid_date_month) %>%
  summarise(number_of_items = sum(number_of_paid_items, na.rm = TRUE)) %>% 
  arrange(paid_date_month)
  
# Subsetting to fit our investigation window 
prescr_monthly <- prescr_monthly %>% 
  filter(paid_date_month >= 202403, paid_date_month <= 202502)

prescr_monthly %>% 
  summarise(rows = n(), min_month = min(paid_date_month), max_month = max(paid_date_month))
## # A tibble: 15 × 4
##    hbt        rows min_month max_month
##    <chr>     <int>     <int>     <int>
##  1 S08000015    12    202403    202502
##  2 S08000016    12    202403    202502
##  3 S08000017    12    202403    202502
##  4 S08000019    12    202403    202502
##  5 S08000020    12    202403    202502
##  6 S08000022    12    202403    202502
##  7 S08000024    12    202403    202502
##  8 S08000025    12    202403    202502
##  9 S08000026    12    202403    202502
## 10 S08000028    12    202403    202502
## 11 S08000029    12    202403    202502
## 12 S08000030    12    202403    202502
## 13 S08000031    12    202403    202502
## 14 S08000032    12    202403    202502
## 15 SB0806        1    202412    202412

1.4 Creating seasonal categories

spr_months_202425 <- c(202403, 202404, 202405)
sum_months_202425 <- c(202406, 202407, 202408)
aut_months_202425 <- c(202409, 202410, 202411)
win_months_202425 <- c(202412, 202501, 202502)

seasons_202425 <- tibble(
  paid_date_month = c(spr_months_202425, sum_months_202425, aut_months_202425, win_months_202425),
  season = c(rep("Spring", length(spr_months_202425)),
             rep("Summer", length(sum_months_202425)),
             rep("Autumn", length(aut_months_202425)),
             rep("Winter", length(win_months_202425))))

1.5 Allocating Met Office regional categorisations to all NHS Scottish Health Boards

# Health Board official NHS names list
hb_names <- read_csv("https://www.opendata.nhs.scot/dataset/9f942fdb-e59e-44f5-b534-d6e17229cc7b/resource/652ff726-e676-4a20-abda-435b98dd7bdc/download/hb14_hb19.csv") %>% 
  clean_names()

# Met Office regional mapping
north_hb <- c("NHS Highland", "NHS Western Isles", "NHS Orkney", "NHS Shetland")
east_hb <- c("NHS Borders", "NHS Lothian", "NHS Fife", "NHS Tayside", "NHS Grampian", "NHS Forth Valley")
west_hb <- c("NHS Ayrshire and Arran", "NHS Dumfries and Galloway", "NHS Greater Glasgow and Clyde", "NHS Lanarkshire")

metoffice_hb_region <- tibble(
  hb_name = c(north_hb, east_hb, west_hb),
  region = c(rep("North", length(north_hb)),
             rep("East", length(east_hb)),
             rep("West", length(west_hb))))

# Health Boards per region according to Met Office scottish territorial classifications
hb_regional <- hb_names %>% 
  full_join(metoffice_hb_region, by = "hb_name") %>% 
  select(-c(hb_date_archived, hb_date_archived, hb_date_enacted, country))

1.6 Loading and organising NHS Health Board population data

From October 2024 data file. The data file chosen is deliberate as October 2024 marks approximately half-way of the investigation window.

hb_pop <- read_csv("https://www.opendata.nhs.scot/dataset/e3300e98-cdd2-4f4e-a24e-06ee14fcc66c/resource/cec9341e-ccba-4c71-afe4-a614f5e97b9f/download/practice_listsizes_oct2024-open-data.csv") %>% 
  clean_names() %>% 
  select(hb, sex, all_ages) %>% 
  filter(!sex %in% c("Male", "Female")) %>%
  group_by(hb) %>% 
  summarise(hb_population = sum(all_ages, na.rm = TRUE)) %>% 
  ungroup()

1.7 Joining prescriptions, Health Board, population, season and regional categorisation to a single object.

prescr_seasonal <- prescr_monthly %>% 
  full_join(hb_regional %>% 
              select(hb, hb_name, region), by = join_by(hbt == hb)) %>% 
  full_join(hb_pop, by = join_by(hbt == hb)) %>% 
  full_join(seasons_202425, by = join_by(paid_date_month))

1.8 Notes at this stage:

  • After the full_join() there are 4 rows with NA in prescription data and population (paid_date_month, number_or_items, and hb_population). Upon further investigation and a look at the object hb_names, these were shown to have had their hbt numbers archived in 2018 and 2019, making them easily removable from our data.
  • There is a row with hbt ‘SB0806’ which shows NA for hb_name though has antidepressant prescription data for December 2024 only. Given the value (‘2’) is extremely low and this hbt is not on record or appears any other time, it has been decided to be excluded from the analysis.
prescr_seasonal <- prescr_seasonal %>% 
  filter(!is.na(hb_name)) %>% 
  filter(!is.na(paid_date_month))

# checking if there is a missing region or population
prescr_seasonal %>% 
  filter(is.na(region) | is.na(hb_population)) # tibble of 0 x 7 shows we have eliminated all NAs
## # A tibble: 0 × 7
## # Groups:   hbt [0]
## # ℹ 7 variables: hbt <chr>, paid_date_month <dbl>, number_of_items <dbl>,
## #   hb_name <chr>, region <chr>, hb_population <dbl>, season <chr>

1.9 Population Weighting for NHS Health Board data

A calculation of items_per_1000_people was be made to allow for population weighting of prescription items. This is because all NHS Health Boards have different populations, thus, comparing their “number_of_items” solely would be affected by population sizes.

prescr_seasonal_standard <- prescr_seasonal %>%
  mutate(items_per_1000 = (number_of_items/hb_population)*1000)

1.10 Introducing Met Office UK daylight hours data and creating a new function

This is somewhat challenging. Given the nature of the files the Met Office has available (.txt), I converted them into .csv files using Excel. These files can be found in the docs\data folder attached.. Upon inspection of the data, one can notice that there are specific columns for each season apart from one for each month. This report uses seasonal data to make the merging processes easier. Building a function had to be done to avoid repeting the same wrangling for each region.

Note: these .csv files contain columns named spr, sum, aut, win and a year column for each year and season

# Reading all CSV files first
daylight_north <- read_table(here("docs", "data", "R_north_scotland_sunshine.csv")) %>%
  clean_names()

daylight_east <- read_table(here("docs","data", "R_east_scotland_sunshine.csv")) %>% 
  clean_names()

daylight_west <- read_table(here("docs","data","R_west_scotland_sunshine.csv")) %>% 
  clean_names()

# Making a function to avoid code repetition for each .csv file
daylight_season_function <- function(data, region_name, year_filter, season_cols = c("win", "spr", "sum", "aut"), year_cols = c("year_12", "year_13", "year_14", "year_15"), full_season_names = c("Winter", "Spring", "Summer", "Autumn")) {

  # built-in checker
  if(length(season_cols)!= length(year_cols)) stop("season_cols and year_cols need to have the same length")
  if(length(season_cols)!= length(full_season_names)) stop("full_season_names and season_cols need to have the same length")
  
  # processing each individual season
  season_list <- map2(season_cols, year_cols, ~ {
    data %>% 
      select(all_of(.x), all_of(.y)) %>% 
      filter(.data[[.y]] == year_filter) %>% 
      rename(year = all_of(.y))
  })
  
  # joining all four seasons together
  season_complete <- reduce(season_list, full_join, by = "year") %>% 
    relocate(all_of(season_cols), .after = last_col()) %>%
    mutate(across(all_of(season_cols), as.numeric)) %>% 
    pivot_longer(cols = all_of(season_cols), names_to = "season", values_to = "daylight_hrs") %>%
    mutate(season = recode(season, !!!set_names(full_season_names, season_cols)),
           region = region_name) %>% 
    arrange(year, factor(season, levels = full_season_names)) %>%
    filter(!is.na(daylight_hrs))
  
  return(season_complete)
}

1.11 Using the daylight_season_function

daylight_season_function() was used to wrangle and select specific data from each Met Office UK regional daylight dataset. all_seasons_daylight contains all daylight hours per season per region for the duration of the investigation window.

# Using the function for each region and making sure the "year" category is a character
season_daylight_north <- daylight_season_function(daylight_north, "North", "2024")
season_daylight_north <- season_daylight_north %>% 
  mutate(year = as.character(year))

season_daylight_east <- daylight_season_function(daylight_east, "East", "2024")
season_daylight_east <- season_daylight_east %>% 
  mutate(year = as.character(year))

season_daylight_west <- daylight_season_function(daylight_west, "West", "2024")
season_daylight_west <- season_daylight_west %>% 
  mutate(year = as.character(year))

# Merging all regional daylight data for each season
all_seasons_daylight <- bind_rows(season_daylight_north, season_daylight_east, season_daylight_west)

all_seasons_daylight
## # A tibble: 12 × 4
##    year  season daylight_hrs region
##    <chr> <chr>         <dbl> <chr> 
##  1 2024  Winter         122. North 
##  2 2024  Spring         375. North 
##  3 2024  Summer         330. North 
##  4 2024  Autumn         230. North 
##  5 2024  Winter         164. East  
##  6 2024  Spring         362. East  
##  7 2024  Summer         453. East  
##  8 2024  Autumn         269. East  
##  9 2024  Winter         137. West  
## 10 2024  Spring         367. West  
## 11 2024  Summer         403. West  
## 12 2024  Autumn         230. West

1.12. Loading and merging SIMD ranking data

The Scottish Index of Multiple Deprivation (SIMD) data was merged, wrangled and saved for later analysis.

# Median rank per Health Board was used to summarise the distribution of deprivation
smid_raw <- read_csv("https://www.opendata.nhs.scot/dataset/78d41fa9-1a62-4f7b-9edb-3e8522a93378/resource/acade396-8430-4b34-895a-b3e757fa346e/download/simd2020v2_22062020.csv") %>% 
  clean_names()

smid_hb <- smid_raw %>% 
  select(hb, simd2020v2rank) %>% 
  group_by(hb) %>% 
  summarise(SMID_median_rank = median(simd2020v2rank, na.rm = TRUE)) %>% 
  ungroup()

# Joining daylight, SMID and seasonal prescription data
analysis_prescr_smid <- prescr_seasonal_standard %>%
  full_join(all_seasons_daylight, by = join_by(season, region)) %>% 
  full_join(smid_hb, by = join_by(hbt == hb))

# Checking if any NAs are present
analysis_prescr_smid %>% 
  summarise(missing_daylight = sum(is.na(daylight_hrs)), missing_smid =  sum(is.na(SMID_median_rank))) # result should show a 14 x 3 tibble with value zero (0) for each
## # A tibble: 14 × 3
##    hbt       missing_daylight missing_smid
##    <chr>                <int>        <int>
##  1 S08000015                0            0
##  2 S08000016                0            0
##  3 S08000017                0            0
##  4 S08000019                0            0
##  5 S08000020                0            0
##  6 S08000022                0            0
##  7 S08000024                0            0
##  8 S08000025                0            0
##  9 S08000026                0            0
## 10 S08000028                0            0
## 11 S08000029                0            0
## 12 S08000030                0            0
## 13 S08000031                0            0
## 14 S08000032                0            0

1.13. Loading and merging Healthboard geospatial data

The NHS Health Boards shapefile was merged, wrangled and saved for geospatial analysis.

hb_shp_geo <- st_read(here("docs","data", "Week6_NHS_healthboards_2019.shp")) %>% 
  clean_names()
## Reading layer `Week6_NHS_healthboards_2019' from data source 
##   `/Users/florenciasolorzano/Documents/data_science/B218332/docs/data/Week6_NHS_healthboards_2019.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 14 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 7564.996 ymin: 530635.8 xmax: 468754.8 ymax: 1218625
## Projected CRS: OSGB36 / British National Grid
analysis_geo_prescr <- analysis_prescr_smid %>% 
  group_by(hbt, season, SMID_median_rank) %>% 
  summarise(av_items_per_1000 = mean(items_per_1000, na.rm = TRUE), av_daylight = mean(daylight_hrs)) %>% 
  ungroup() %>% 
  full_join(hb_shp_geo, by = join_by(hbt == hb_code)) %>% 
  st_as_sf()

2. Data Analysis - Results

A seasonal summary table by region using gt():

full_seasonal_table <- analysis_prescr_smid %>% 
  group_by(region, season) %>% 
  summarise(av_daylight = mean(daylight_hrs, na.rm = TRUE), av_items_per_1000 = mean(items_per_1000, na.rm = TRUE)) %>%
  ungroup() %>% 
  arrange(region, factor(season, levels = c("Spring", "Summer", "Autumn", "Winter")))

full_seasonal_table %>%
  mutate(av_items_per_1000 = round(av_items_per_1000, 2),
         av_daylight = round(av_daylight, 2)) %>% 
  gt(groupname_col = "region") %>% 
  cols_label(
    season = md("Season"),
    av_daylight = md("Mean Total Daylight (hrs)"),
    av_items_per_1000 = md("Mean Prescriptions (units/1000 people)")) %>% 
  tab_header(
    title = md("Antidepressant Prescriptions per 1000 and Total Daylight hours by region"),
    subtitle = "March 1st, 2024 - February 28th, 2025") %>% 
  fmt_number(columns = c(av_items_per_1000, av_daylight), decimals = 2)
Antidepressant Prescriptions per 1000 and Total Daylight hours by region
March 1st, 2024 - February 28th, 2025
Season Mean Total Daylight (hrs) Mean Prescriptions (units/1000 people)
East
Spring 361.80 113.06
Summer 452.60 113.84
Autumn 268.60 113.79
Winter 163.60 114.51
North
Spring 374.60 121.67
Summer 330.40 120.00
Autumn 229.70 121.45
Winter 121.90 121.96
West
Spring 367.40 137.25
Summer 403.10 138.44
Autumn 230.30 138.47
Winter 137.20 139.04

This table shows the mean seasonal total daylight hours and the mean antidepressant prescriptions per 1000 population for each region. This is the numeric anchor for the following data visualisations: a) regional dual bar seasonal plot b) seasonal heat map c) deprivation vs prescribing scatter plot

3. Data Visualisation

3.1. Figure 1: Regional Dual Bar Seasonal Plot

Figure 1 shows a dual bar chart graph displaying antidepressant prescriptions per 1000 people proportional to each NHS Health Board population and average daylight hours per month from March 2024 to March 2025, faceted by Scottish Geographical Region (North, East, and West).

dual_bar_analysis <- analysis_prescr_smid %>% 
  group_by(region, season) %>% 
  summarise(av_items_per_1000 = mean(items_per_1000, na.rm = TRUE), av_daylight = mean(daylight_hrs, na.rm = TRUE)) %>% 
  ungroup() %>% 
  mutate(season = factor(season, levels = c("Spring", "Summer", "Autumn", "Winter"))) %>% 
  pivot_longer(cols = c(av_daylight, av_items_per_1000),
               names_to = "variable",
               values_to = "value")

# Making a ggplot with double bars
dual_bar_plot <- dual_bar_analysis %>% 
  ggplot(aes(x = season, y = value, fill = variable, text = ifelse(
    variable == "av_daylight",
    paste0("Daylight (hrs): ", round(value, 2)),
    paste0("Prescriptions: ", round(value, 2))))) +
  geom_col(position = position_dodge(width = 0.9), alpha = 0.8) +
  facet_wrap(~region, nrow = 1, scales = "free_x") +
  scale_y_continuous(
    name = str_wrap("Average Total Daylight (hrs)", width = 30),
    breaks = seq(0, max(dual_bar_analysis$value), 50),
    sec.axis = sec_axis(~ ., name = str_wrap("Average Antidepressant Prescriptions (units/1000 people)", width = 35), breaks = seq(0, max(dual_bar_analysis$value), 50))) +
  scale_fill_manual(values = c("orange", "skyblue"), 
                    labels = c("Daylight (hrs)", "Prescriptions (units/1000)")) +
  labs(
    title = str_wrap("Average seasonal total daylight hours and average antidepressant prescription items by region", width = 63),
    subtitle = "Prescriptions per 1000 people across Scottish Health Boards",
    x = "Season",
    fill = "") +
  theme_minimal(base_size = 13) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
    legend.position = "top",
    legend.title = element_blank(),
    legend.text = element_text(size = 10),
    strip.background = element_rect(fill = "gray90", color = NA),
    strip.text = element_text(face = "bold", size = 12),
    plot.title = element_text(face = "bold", size = 16, margin = margin(b = 5), hjust = 0.3),
    plot.title.position = "panel",
    plot.subtitle = element_text(face = "bold", size = 12, margin = margin(b = 10), hjust = 0.5),
    panel.grid.major.x = element_blank(),
    panel.grid.minor = element_blank(),
    plot.margin = margin(t = 20, r = 10, b = 40, l = 10, unit = "pt"))

dual_bar_plot

Prescriptions seem to marginally increase from Spring to Winter. However, summer seasons across regions don’t show the lowest average antidepressant prescriptions. It seems that antidepressant prescription trends increase as the seasonal year progresses.

3.2. Figure 2 : Chropleth plot of antidepressant prescriptions and total seasonal daylight hours per Health Board

Figure 2 shows geospatial visualisation of prescriptions per season per Health Board, where two choropleth maps have been faceted to facilitate seasonal comparisons.

map_prescr_seasons <- analysis_geo_prescr %>% 
  ggplot() +
  geom_sf(aes(fill = av_items_per_1000), size = 0.15, color = "darkgrey") +
  scale_fill_distiller(palette = "Blues", direction = 1) +
  facet_wrap(~season, nrow = 1) +
   labs(title = "Seasonal Antidepressant Prescriptions (March 2024 - February 2025)", subtitle = "Prescriptions by Scottish Health Board per 1000 people comparable with Average Total Daylight (hrs)", fill = "units/1000 people") +
  theme_void() +
  theme(
    plot.title = element_text(face = "bold", size = 10, hjust = 0.5),
    plot.subtitle = element_text(size = 9, margin = margin(t = 10, b = 20), hjust = 0.5), 
    legend.title = element_text(face = "bold", size = 10))

map_daylight_seasons <- analysis_geo_prescr %>% 
  ggplot() +
  geom_sf(aes(fill = av_daylight), size = 0.15, colour = "darkgrey") +
  scale_fill_distiller(palette = "Oranges", direction = 1) +
  facet_wrap(~season, nrow = 1) +
  labs(fill = "Av. Total Daylight (hrs)") +
  theme_void() +
  theme(legend.title = element_text(face = "bold", size = 10))

full_map_plot <- map_prescr_seasons / map_daylight_seasons +
  plot_layout(heights = c(1,1))

full_map_plot

Seems like the southernmost Health Boards are situated, the higher the anitdepressant prescriptions per 1000 people there are. The choropleth map seems to show this as almost uniform despite seasonal changes despite varying daylight hours. The northernmost regions are not the ones with most prescriptions despite having the lowest total average daylight hours overall.
With this in mind, this investigation finishes with the alternative investigation of the third alternative hypothesis postulated initially about SMID ranks and antidepressant prescriptions during varying daylight seasons

3.3. Figure 3: Bivariate Map of the relatedness between SIMD rankings and antidepressant prescriptions per season

Figure 3 shows geospatial visualisation of whether SIMD rankings and antidepressant prescriptions are correlated.

# Bivariate map bins
biv_bins <- analysis_geo_prescr %>% 
  mutate(prescr_bin = ntile(av_items_per_1000,3),
         smid_bin = ntile(SMID_median_rank, 3),
         biv_class = paste0(prescr_bin, "-", smid_bin))

# bivariate map bin colours
biv_palette <- c(
  "1-1" = "#e8e8e8",
  "2-1" = "#b8d6be",
  "3-1" = "#64acbe",
  "1-2" = "#d4b9da",
  "2-2" = "#a5add3",
  "3-2" = "#4a7bb7",
  "1-3" = "#c994c7",
  "2-3" = "#df65b0",
  "3-3" = "#dd1c77")

# bivariate map bin matrix
biv_matrix <- expand.grid(prescr_bin = 1:3, smid_bin = 1:3) %>% 
  mutate(smid_bin = smid_bin, biv_class = paste0(prescr_bin, "-", smid_bin))

# bivariate map legend
biv_legend <- biv_matrix %>% 
  ggplot(aes(x = prescr_bin, y = smid_bin, fill = biv_class)) +
  geom_tile(color = "white") +
  scale_fill_manual(values = biv_palette, guide = "none") +
  scale_y_continuous(breaks = 1:3, labels = c("Low", "", "High")) +
  scale_x_continuous(breaks = 1:3, labels = c("Low", "", "High")) +
  labs(
    x = "Prescriptions",
    y = "SMID Rank") +
  coord_fixed(ratio = 1)+
  theme_minimal(base_size = 9) +
  theme_void()+
  theme(
    axis.title = element_text(size = 8, face = "bold"),
    axis.text = element_text(size = 6),
    panel.grid = element_blank(),
    plot.margin = margin(t = 0, r = 5, b = 0, l = 0))

# bivariate map
map_biv_solo <- biv_bins %>% 
  ggplot() +
  geom_sf(aes(fill = biv_class), size = 0.1, colour = "white") +
  scale_fill_manual(values = biv_palette, guide = "none") +
  facet_wrap(~season, nrow = 1) +
  labs(
    title = str_wrap("Seasonal Antidepressant prescriptions comparison with Median Deprivation Rankings"),
    subtitle = str_wrap("Prescriptions per 1000 people across Scottish NHS Health Boards | Scottish Multiple Index of Deprivation (SMID) Rank"),
    fill = "Bivariate classification") +
  coord_sf(expand = FALSE) +
  theme_void()+
  theme(
    plot.title = element_text(face = "bold", size = 14, margin = margin(r = 0.5, b = 0.5), hjust = 0.5),
    plot.title.position = "panel",
    plot.subtitle = element_text(size = 10, margin = margin(t = 10, b = 20), hjust = 1),
    panel.spacing = unit(0.1, "lines"),
    strip.text = element_text(face = "bold", size = 8),
    plot.margin = margin(t = 0, r = 10, b = 0, l = 10, unit = "pt"))

full_bivariate_map <- plot_grid(map_biv_solo, biv_legend,
                           ncol = 2,
                           rel_widths = c(4, 1),
                           align = "t")

full_bivariate_map

Lower SIMD ranks indicate more deprived Health Board populations. The general trend seems to be that the higher the SMID rank, the lesser the antidepressant prescriptions per Health Board. Only significant differences have been shown when comparing summer and winter, which do indicate that higher baseline prescribing is delivered more in deprived areas during the season with least sunshine throughout the year. This is also corroborated in the Figure 5 in the Appendix 1.4. which has been included for interest.

Conclusions

There is evidence suggesting that antidepressant prescriptions increase during the winter period in several Health Board regions across Scotland. However, this remains an inference as statistical testing must be done to confirm significant differences between seasons.
Furthermore, Health board regional (latitudinal) differences exist. Northernmost boards with the least total average daylight show seasonal patterns most strikingly when comparing summer and winter data. These are visually consistent with the hypothesis.
Finally, deprived areas (lower SIMD rank) somewhat shows higher baseline prescribing. However, the introduction of this factor demonstrate that Deprivation superimposes seasonality, suggesting the social factors are also equally if not more important than environmental factors when researching depressive disorders within the population as a whole.

Limitations and Next Steps

Limitations

  • Prescriptions have been used as a direct inference for mental health burden, meaning that these have missed undiagnosed or untreated cases and have included prescriptions that are not necessarily just utilised for depressive disorders (i.e. ADHD)
  • The investigation window only spans a year (March 1st, 2024 - 28th February, 2025) which limits the assessment of the annual variability of results and trends.
  • Daylight data was aggregated (Total daylight hours) suggesting that it might not reflect direct exposure or monthly trends within seasons.
  • SMID rankings were summarised through medians at the Health Board level which potentially masks zonal differences within Health Boards.

Further Study

This is a promising scope of study, hence, future research must be done to tackle how Seasonal Affective Disorder (SAD) tackles Scottish residence heterogeneously. Next steps are the following: - The study must be replicated over multiple years (5-10 years) to assess if these trends stand, or more so, if these have changed over time. This is elemental for the introduction of other socio-environmental factors to the study. - GP practice as datazones could be used to reduce bias and generalisations when summarising data. - As suggested beforehand, there are multiple factors affecting SAD (i.e. age demographics, prescription policies, etc), hence future studies should: - Control for compounding factors - Consider distinct research models. i.e. Mixed effects models

References

Appendix

Appendix 1.2. Season Categorisation as per Met Office UK Guidelines

Spring: March, April, May
Summer: June, July, August
Autumn: September, October, November
Winter: December, January, February

Appendix 1.3. NHS Health Board Geographical Categorisation

This categorisation was made according to the Met Office’s territorial delineation of Scotland based on the distribution of climate measurements as available in their website.

Northern Scotland: NHS Highland, NHS Western Isles, NHS Orkney, NHS Shetland
Eastern Scotland: NHS Borders, NHS Lothian, NHS Fife, NHS Tayside, NHS Grampian, NHS Forth Valley
Western Scotland: NHS Ayrshire and Arran, NHS Dumfries and Galloway, NHS Greater Glasgow and Clyde, NHS Lanarkshire

Appendix 1.4. Figure 5

# Interactive scatter plot
scatter_plot_data <- analysis_geo_prescr %>%
  st_drop_geometry() %>% 
  filter(!is.na(SMID_median_rank), !is.na(av_items_per_1000))
  
scatter_plot <- plot_ly(
  data = scatter_plot_data,
  x = ~SMID_median_rank,
  y = ~av_items_per_1000,
  type = "scatter",
  mode = "markers",
  color = ~hb_name,
  text = ~paste0(
    "HB:NHS ", hb_name, "<br>",
    "Season: ", season, "<br>",
    "Prescriptions/1000: ", round(av_items_per_1000,1), "<br>",
    "Av. Total Daylight (hrs): ", round(av_daylight, 1)),
    hoverinfo = "text",
    marker = list(size = 10, opacity = 0.8)) %>%
    layout(
      title = list(text = "Antidepressant Prescriptions according to Median Deprivation Ranks",
                   font = list(size = 14),
                   x = 0.5),
      annotations = list(list(
        x = 0.5,
        y = 1.05,
        text = "Prescriptions per 1000 people across Scottish NHS Health Boards | Scottish Multiple Index of Deprivation (SMID) Rank",
        xref = "paper",
        yref = "paper",
        showarrow = FALSE,
        font = list(size = 10),
        xanchor = "center",
        yanchor = "bottom")),
      xaxis = list(title = "SMID Rank (Higher = Less Deprived)"),
      yaxis = list(title = "Antidepressant Prescriptions (units/1000)"),
      margin = list (t = 90, r = 100, b = 85, l = 85))
  
  scatter_plot